Breaking down the microbiology world one bite at a time
Count von Count counts all the phages
Do you still remember Count von Count from the children’s TV series Sesame Street? This vampire muppet is teaching kids how to count, and, frankly, he is very good at it! While it might be amusing for kids to watch a math-obsessed, blood-drinking muppet on TV, it turns out that counting is vital for almost every aspect of our lives. Math not only represents the foundation for economy and science, but it’s also a crucial tool for microbes that helps them to make decisions.
But how can organisms that are smaller than a human neuron count? Throughout the evolution they developed different strategies, and this article will talk about one of them. To be more specific, we will focus here on the ability of phage Lambda to count.
The topic of phages has already been covered in multiple MicroBites articles because their biology is just fascinating. A brief recap: Phages are viruses that infect bacteria and archaea. They represent an extremely diverse group of microorganisms, and can be found wherever prokaryotes can be found. One very well characterized phage is Lambda, which infects the model bacterium Escherichia coli.
Like many viruses, phage Lambda has two ways of proliferation (Figure 1). The lytic cycle is what most people might associate with a viral infection. The virus enters a cell, hijacking its molecular machinery to produce copies of itself and then – at a certain point – lyses the cell to release hundreds of new viruses to the environment ready for infection. The lysogenic cycle, on the other hand, is a bit more elaborate. The virus enters a cell, but it does not abuse the cell’s resources to just replicate itself endlessly. Instead, it inserts its own genome – called a prophage – into the host’s genome. This is a dormant state of the virus, which waits to wake up and go into the lytic cycle. How does a virus know which way to choose, lytic or lysogenic? Well, it seems like Tianyou Yao and his colleagues got a bit closer to answering this question.
It has long been discussed that there is a numerical correlation between viruses and host cells. The standing hypothesis states that the ratio of viruses to host cells decides which way will be followed. It means that multiple phages entering a host cell simultaneously trigger the lysogenic pathway. This is known as the multiplicity of infection (MOI). The logic behind this is easy: If multiple viruses can infect one cell at a time, the ratio of viruses to victim cells has to be very high. Therefore, it makes no sense for the virus to produce even more copies of itself. Instead, it chooses to embed itself into the host’s genome and wait for a change in the environment.
The aspect that has been unclear so far is how the phage counts the MOI, and how it does this while replicating its own genome at the same time. To solve this riddle, the research group led by Tianyou Yao assessed the messenger RNA (mRNA) levels of three viral genes – called cI, cII and cro – in E. coli cells infected by phage Lambda. These three genes are influencing each other in a complex network of activation and repression loops (Figure 2). The specific ratio of the expression of one to another leads either into the lytic or into the lysogenic trajectory. High expression rate of cro leads towards the lytic trajectory, while a high expression of cI promotes the lysogenic pathway.
To rule out the influence of the phage’s self-replication, which normally starts immediately after entering the host cell, the researchers first tested a Lambda variant that is not able to replicate (non-replicating). Upon infection with this modified phage, they counted the number of viral genomes in a single E. coli cell as well as the number of the mRNAs of the three genes of interest in order to see what the influence of the MOI would be on the mRNA levels of the genes and, in consequence, the trajectory of the viral infection.
Surprisingly, the researchers found out that an expression of genes for the lytic trajectory (cro) were never activated to an extent high enough to go down this route. On the other hand, they found that genes for the lysogenic pathway were upregulated with increasing MOI. Remarkably, no decision for one or the other trajectory could be made by the non-replicating phages at low MOI!
Based on their own laboratory results as well as literature data, the researchers set-up mathematical models to describe the behavior of non-replicating and replicating phages.By using this model, the researchers showed that only infection with exactly one phage per host cell (MOI = 1) leads into the lytic pathway, while any higher MOI will trigger the lysogenic cycle. You could say that phage lambda is a bit opposite to most humans: It likes to party on its own, but will become very sleepy when there is company.
But what’s the reason for this behavior? Tianyou Yao et al. demonstrated a delicate interplay of activation and repression of different promoter regions (genetic elements that promote the transcription of a certain gene), which are dependent on the initial MOI. A higher MOI stimulates a proportionally higher activation of the gene cI, which, in turn, represses the transcription of the cro gene (Remember: high levels of cro would lead towards the lytic trajectory). The virus, then, goes down the lysogenic route. Therefore, even the smallest and genetically simple beings on our planet developed a sophisticated system of sensing each other’s presence.
However, don’t forget that the efforts undertaken by the researchers to unravel this riddle also deserve a round of applause. The work shows how far modern biochemical techniques have developed, and how powerful the combination of data from such experiments with computational models can be. Even Count von Count would be impressed with these mathematical tricks!
Link to the original post: Bacteriophage self-counting in the presence of viral replication Tianyou Yao, Seth Coleman, Thu Vu Phuc Nguyen, Ido Golding, Oleg A. Igoshin Proceedings of the National Academy of Sciences Dec 2021, 118 (51) e2104163118; DOI: 10.1073/pnas.2104163118